The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XL-2/W3, 2014 The 1st ISPRS International Conference on Geospatial Information Research, 15–17 November 2014, Tehran, Iran
INVESTIGATING THE RELATION BETWEEN PREVALENCE OF ASTHMATIC ALLERGY WITH THE CHARACTERISTICS OF THE ENVIRONMENT USING ASSOCIATION RULE MINING Y. Kanani Sadat a,*, F. Karimipour a, A. Kanani Sadatb a
Dept. of Surveying and Geomatics Engineering, College of Engineering, University of Tehran, Iran - (yousefkanani, fkarimipr)@ut.ac.ir b Dept. of Civil Engineering, Islamic Azad University, Ahar branch, Iran -
[email protected]
KEY WORDS: Spatial Association Rule Mining; Environmental Characteristics; Asthmatic Allergy; Air Pollution; Apriori
ABSTRACT: The prevalence of allergic diseases has highly increased in recent decades due to contamination of the environment with the allergy stimuli. A common treat is identifying the allergy stimulus and, then, avoiding the patient to be exposed with it. There are, however, many unknown allergic diseases stimuli that are related to the characteristics of the living environment. In this paper, we focus on the effect of air pollution on asthmatic allergies and investigate the association between prevalence of such allergies with those characteristics of the environment that may affect the air pollution. For this, spatial association rule mining has been deployed to mine the association between spatial distribution of allergy prevalence and the air pollution parameters such as CO, SO2, NO2, PM10, PM2.5, and O3 (compiled by the air pollution monitoring stations) as well as living distance to parks and roads. The results for the case study (i.e., Tehran metropolitan area) indicates that distance to parks and roads as well as CO, NO2, PM10, and PM2.5 is related to the allergy prevalence in December (the most polluted month of the year in Tehran), while SO 2 and O3 have no effect on that.
1. INTRODUCTION Prevalence of allergic diseases has highly increased in recent decades, especially among children, due to modern living conditions resulted in contamination of the environment with the allergy stimuli, called allergen (Ng et al., 2009; Zöllner et al., 2005). Allergic patients have hypersensitive immune systems that abnormally react to harmless substances. Several factors cause allergic reactions, which depend on the gene, living style and habits, foods, as well as the geography and conditions of the environment (Asher et al., 1995). A common treat to allergic diseases is identifying the allergen and, then, avoiding the patient to be exposed with it (Douglass and O Hehir, 2006). There are, however, several unknown stimuli that may cause allergic diseases, many of which are related to the characteristics of the living environment. Therefore, analyzing the data collected about the living environment of allergic patients may lead to identifying the role of environmental parameters in prevalence of allergies. As the patients are distributed in the space, and the relation varies with time, the spatio-temporal data mining techniques seems very efficient in this regards. Spatial data mining concerns development and application of novel computational techniques to analyze very large spatial databases (Buttenfield et al., 2001; Koperski et al., 1996). A major distinction of spatial data mining is that attributes of the neighboring objects influence each other and thus must be taken in to account. Furthermore, the location and extension of spatial objects define implicit relations of spatial neighborhoods (such as topological, distance and directional relations), which are used by spatial data mining algorithms (Miller and Han, 2001). In this paper, we focus on the effect of air pollution on asthmatic allergies and investigate the relation between prevalence of such allergies with those characteristics of the
environment that may affect the air pollution. The reside location of a group of asthmatic allergic patients, live in Tehran metropolitan area, as well as spatial characteristics of the environment (e.g., location of parks, roads and air pollution monitoring stations) were placed on the map. We, then, deployed spatial association rule mining (as one of the spatial data mining analyses) to extract the association between asthmatic allergy prevalence and the air pollution parameters such as CO (carbon monoxide), SO2 (sulfur dioxide), NO2 (nitrogen dioxide), PM10 and PM2.5 (particulate matter with a diameter of
